An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

EACL 2021  ·  Markus Eberts, Adrian Ulges ·

We present a joint model for entity-level relation extraction from documents. In contrast to other approaches - which focus on local intra-sentence mention pairs and thus require annotations on mention level - our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. We achieve state-of-the-art relation extraction results on the DocRED dataset and report the first entity-level end-to-end relation extraction results for future reference. Finally, our experimental results suggest that a joint approach is on par with task-specific learning, though more efficient due to shared parameters and training steps.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Joint Entity and Relation Extraction DocRED JEREX Relation F1 40.38 # 4
Relation Extraction DocRED JEREX-BERT-base F1 60.40 # 29
Ign F1 58.44 # 29
Relation Extraction ReDocRED JEREX F1 72.57 # 6
Ign F1 71.45 # 6


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